English

Inducing Approximately Optimal Flow Using Truthful Mediators

Computer Science and Game Theory 2015-05-11 v2

Abstract

We revisit a classic coordination problem from the perspective of mechanism design: how can we coordinate a social welfare maximizing flow in a network congestion game with selfish players? The classical approach, which computes tolls as a function of known demands, fails when the demands are unknown to the mechanism designer, and naively eliciting them does not necessarily yield a truthful mechanism. Instead, we introduce a weak mediator that can provide suggested routes to players and set tolls as a function of reported demands. However, players can choose to ignore or misreport their type to this mediator. Using techniques from differential privacy, we show how to design a weak mediator such that it is an asymptotic ex-post Nash equilibrium for all players to truthfully report their types to the mediator and faithfully follow its suggestion, and that when they do, they end up playing a nearly optimal flow. Notably, our solution works in settings of incomplete information even in the absence of a prior distribution on player types. Along the way, we develop new techniques for privately solving convex programs which may be of independent interest.

Keywords

Cite

@article{arxiv.1502.04019,
  title  = {Inducing Approximately Optimal Flow Using Truthful Mediators},
  author = {Ryan Rogers and Aaron Roth and Jonathan Ullman and Zhiwei Steven Wu},
  journal= {arXiv preprint arXiv:1502.04019},
  year   = {2015}
}

Comments

Version with latencies not normalized

R2 v1 2026-06-22T08:29:08.698Z